Clara Lucía Galimberti

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The work presented in this thesis lies at the intersection of Machine Learning (ML) and control theory. The first part is dedicated to the design of Deep Neural Network (DNN) architectures with specific numerical properties, inspired by dynamical systems m ...

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost t ...

We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractability reasons, this problem is typically addressed by minimizing an empirical ...

Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/explod- ing gradients during weight optimization. To avoid this problem, we propose a class of DNNs stemming from the time discretization of Hamiltonian systems. The t ...

In this paper, we propose a suboptimal distributed LQR control method, applicable to systems coupled through both physical interconnections and the quadratic cost to be minimized. Thanks to a novel suboptimal but distributed cost-to-go matrix update that e ...

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

Clara Lucía Galimberti

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Navigating the Loop: From Control Systems to Neural Networks and Back

Parametrizations of all stable closed-loop responses: From theory to neural network control design

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

A unified framework for Hamiltonian deep neural networks

Suboptimal Distributed LQR Design for Physically Coupled Systems

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Erratum to “Learning to Boost the Performance of Stable Nonlinear Systems”